For all experiments in this section, we report the average and standard deviation over ten runs with different random seeds.
The results are reported on the the best model selected using validation data.
We set hyper-parameters of the used techniques and considered baseline methods, e.g., the batch size, the number of hidden units, the optimizer, and the learning rate as suggested by their authors.
We use the recent MIPS method ScaNN [1]} in the implementation of our Flashlight.
For the hyper-parameters of our Flashlight, we have found in the experiments that the performance of Flashlight is robust to the change of hyper-parameters in a board range.
Therefore, we simply set the number of iterations of our Flashlight as \(T = 3\)  and the number of retrieved neighbors constant as 200 per iteration by default.
We run all experiments on a machine with 80 Intel(R) Xeon(R) E5-2698 v4 @ 2.20GHz CPUs, and a single NVIDIA V100 GPU with 16GB RAM.
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